scholarly journals A novel wavelet stereo matching method to improve DEM accuracy generated from SPOT stereo image pairs

2002 ◽  
Author(s):  
Yan Xia ◽  
Anthony Tung Shuen Ho ◽  
YanWen Ji
Keyword(s):  
Stereo Matching ◽  
Stereo Image ◽  
Matching Method ◽  
Image Pairs ◽  
Dem Accuracy

scholarly journals Efficient Stereo Matching with Decoupled Dissimilarity Measure Using Successive Weighted Summation

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Cheng-Tao Zhu ◽  
Yau-Zen Chang ◽  
Huai-Ming Wang ◽  
Kai He ◽  
Shih-Tseng Lee ◽  
...  
Keyword(s):  
Stereo Matching ◽  
Dissimilarity Measure ◽  
Stereo Image ◽  
Fitting Procedure ◽  
Comparable Performance ◽  
Novel Method ◽  
Disparity Maps ◽  
Image Pairs ◽  
Benchmark Evaluation ◽  
Complexity Algorithm

Developing matching algorithms from stereo image pairs to obtain correct disparity maps for 3D reconstruction has been the focus of intensive research. A constant computational complexity algorithm to calculate dissimilarity aggregation in assessing disparity based on separable successive weighted summation (SWS) among horizontal and vertical directions was proposed but still not satisfactory. This paper presents a novel method which enables decoupled dissimilarity measure in the aggregation, further improving the accuracy and robustness of stereo correspondence. The aggregated cost is also used to refine disparities based on a local curve-fitting procedure. According to our experimental results on Middlebury benchmark evaluation, the proposed approach has comparable performance when compared with the selected state-of-the-art algorithms and has the lowest mismatch rate. Besides, the refinement procedure is shown to be capable of preserving object boundaries and depth discontinuities while smoothing out disparity maps.

scholarly journals Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3747 ◽  
Author(s):  
Ma ◽  
Bai ◽  
Wang ◽  
Fang
Keyword(s):  
Nonlinear Optimization ◽  
Stereo Matching ◽  
Measurement Model ◽  
Stereo Image ◽  
Measurement Unit ◽  
Experimental Comparison ◽  
The Public ◽  
Stereo Cameras ◽  
Image Pairs ◽  
The Cost

The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.

scholarly journals Real-Time FPGA Accelerated Stereo Matching for Temporal Statistical Pattern Projector Systems

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6435
Author(s):  
Zan Brus ◽  
Marko Kos ◽  
Matic Erker ◽  
Iztok Kramberger
Keyword(s):  
Real Time ◽  
Stereo Matching ◽  
Input Image ◽  
Stereo Image ◽  
Image Pair ◽  
Performance Per Watt ◽  
Statistical Pattern ◽  
And Performance ◽  
Image Pairs ◽  
High Level

The presented paper describes a hardware-accelerated field programmable gate array (FPGA)–based solution capable of real-time stereo matching for temporal statistical pattern projector systems. Modern 3D measurement systems have seen an increased use of temporal statistical pattern projectors as their active illumination source. The use of temporal statistical patterns in stereo vision systems includes the advantage of not requiring information about pattern characteristics, enabling a simplified projector design. Stereo-matching algorithms used in such systems rely on the locally unique temporal changes in brightness to establish a pixel correspondence between the stereo image pair. Finding the temporal correspondence between individual pixels in temporal image pairs is computationally expensive, requiring GPU-based solutions to achieve real-time calculation. By leveraging a high-level synthesis approach, matching cost simplification, and FPGA-specific design optimizations, an energy-efficient, high throughput stereo-matching solution was developed. The design is capable of calculating disparity images on a 1024 × 1024(@291 FPS) input image pair stream at 8.1 W on an embedded FPGA platform (ZC706). Several different design configurations were tested, evaluating device utilization, throughput, power consumption, and performance-per-watt. The average performance-per-watt of the FPGA solution was two times higher than in a GPU-based solution.

scholarly journals REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

2016 ◽  
Vol III-3 ◽  
pp. 123-130 ◽  
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Search Space ◽  
Image Resolution ◽  
Stereo Image ◽  
Digital Cameras ◽  
Dense Stereo Matching ◽  
Dense Matching ◽  
Optimization Stage ◽  
Image Pairs

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

scholarly journals REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

2016 ◽  
Vol III-3 ◽  
pp. 123-130
Author(s):  
M. Shahbazi ◽  
G. Sohn ◽  
J. Théau ◽  
P. Ménard
Keyword(s):  
Stereo Matching ◽  
Belief Propagation ◽  
Search Space ◽  
Image Resolution ◽  
Stereo Image ◽  
Digital Cameras ◽  
Dense Stereo Matching ◽  
Dense Matching ◽  
Optimization Stage ◽  
Image Pairs

Dense stereo matching is one of the fundamental and active areas of photogrammetry. The increasing image resolution of digital cameras as well as the growing interest in unconventional imaging, e.g. unmanned aerial imagery, has exposed stereo image pairs to serious occlusion, noise and matching ambiguity. This has also resulted in an increase in the range of disparity values that should be considered for matching. Therefore, conventional methods of dense matching need to be revised to achieve higher levels of efficiency and accuracy. In this paper, we present an algorithm that uses the concepts of intrinsic curves to propose sparse disparity hypotheses for each pixel. Then, the hypotheses are propagated to adjoining pixels by label-set enlargement based on the proximity in the space of intrinsic curves. The same concepts are applied to model occlusions explicitly via a regularization term in the energy function. Finally, a global optimization stage is performed using belief-propagation to assign one of the disparity hypotheses to each pixel. By searching only through a small fraction of the whole disparity search space and handling occlusions and ambiguities, the proposed framework could achieve high levels of accuracy and efficiency.

Analysis of Cluttered Scenes Using an Elastic Matching Approach for Stereo Images

2006 ◽  
Vol 18 (6) ◽  
pp. 1441-1471 ◽  
Author(s):  
Christian Eckes ◽  
Jochen Triesch ◽  
Christoph von der Malsburg
Keyword(s):  
Graph Matching ◽  
Recognition Performance ◽  
Stereo Image ◽  
Matching Algorithm ◽  
Automatic Interpretation ◽  
Color Features ◽  
Occluded Objects ◽  
Elastic Graph Matching ◽  
Image Pairs ◽  
Object Models

We present a system for the automatic interpretation of cluttered scenes containing multiple partly occluded objects in front of unknown, complex backgrounds. The system is based on an extended elastic graph matching algorithm that allows the explicit modeling of partial occlusions. Our approach extends an earlier system in two ways. First, we use elastic graph matching in stereo image pairs to increase matching robustness and disambiguate occlusion relations. Second, we use richer feature descriptions in the object models by integrating shape and texture with color features. We demonstrate that the combination of both extensions substantially increases recognition performance. The system learns about new objects in a simple one-shot learning approach. Despite the lack of statistical information in the object models and the lack of an explicit background model, our system performs surprisingly well for this very difficult task. Our results underscore the advantages of view-based feature constellation representations for difficult object recognition problems.

scholarly journals A comparison of semiglobal and local dense matching algorithms for surface reconstruction

2014 ◽  
Vol XL-5 ◽  
pp. 187-194 ◽  
Author(s):  
E. Dall'Asta ◽  
R. Roncella
Keyword(s):  
Computer Vision ◽  
Stereo Matching ◽  
Least Square ◽  
Stereo Image ◽  
Dense Matching ◽  
Consistency Constraints ◽  
Global Matching ◽  
Matching Cost ◽  
Software Codes ◽  
Matching Techniques

Encouraged by the growing interest in automatic 3D image-based reconstruction, the development and improvement of robust stereo matching techniques is one of the most investigated research topic of the last years in photogrammetry and computer vision.<br><br> The paper is focused on the comparison of some stereo matching algorithms (local and global) which are very popular both in photogrammetry and computer vision. In particular, the Semi-Global Matching (SGM), which realizes a pixel-wise matching and relies on the application of consistency constraints during the matching cost aggregation, will be discussed.<br><br> The results of some tests performed on real and simulated stereo image datasets, evaluating in particular the accuracy of the obtained digital surface models, will be presented. Several algorithms and different implementation are considered in the comparison, using freeware software codes like MICMAC and OpenCV, commercial software (e.g. Agisoft PhotoScan) and proprietary codes implementing Least Square e Semi-Global Matching algorithms. The comparisons will also consider the completeness and the level of detail within fine structures, and the reliability and repeatability of the obtainable data.

Disparity Surface Reconstruction Based on a Stereo Light Microscope and Laser Fringes

2018 ◽  
Vol 24 (5) ◽  
pp. 503-516
Author(s):  
Yuezong Wang
Keyword(s):  
Surface Reconstruction ◽  
Light Microscope ◽  
Stereo Matching ◽  
Block Matching ◽  
Stereo Image ◽  
Reference Plane ◽  
Motion Direction ◽  
Measuring Techniques ◽  
Novel Method ◽  
Disparity Contours

AbstractMicroscopic vision systems based on a stereo light microscope (SLM) are used in microscopic measuring fields. Conventional measuring methods output the disparity surface based on stereo matching methods; however, these methods require that stereo images contain sufficient distinguishing features. Moreover, matching results typically contain many mismatched points. This paper presents a novel method for disparity surface reconstruction by combining an SLM and laser measuring techniques. The surfaces of small objects are scanned by a laser fringe, and a stereo image sequence containing laser stripes is obtained. The central contours of the laser stripes are extracted, and central contours are derived for alignment. A disparity coordinate system is then defined and used to analyze the relationship between the motion direction and reference plane. Next, the method of aligning disparity contours is proposed. The results show that our method can achieve a precision of ±0.5 pixels and that the real and measured shapes described by the disparity surface are consistent based on our method. Our method is confirmed to perform much better than the conventional block-matching method. The disparity surface output obtained by our method can be used to measure the surface profiles of microscopic objects accurately.

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